Audience
Anyone looking for an open-source deep learning framework with expression, speed and modularity
About Caffe
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.
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Pricing
Free Version:
Free Version available.
Company Information
BAIR
United States
caffe.berkeleyvision.org
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Product Details
Platforms Supported
Cloud
Training
Documentation
Support
Online
Caffe Frequently Asked Questions
Caffe Product Features
Deep Learning
Document Classification
Visualization
Convolutional Neural Networks
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
